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Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine
Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destr...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268769/ https://www.ncbi.nlm.nih.gov/pubmed/35808225 http://dx.doi.org/10.3390/s22134729 |
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author | Pech-May, Fernando Aquino-Santos, Raúl Rios-Toledo, German Posadas-Durán, Juan Pablo Francisco |
author_facet | Pech-May, Fernando Aquino-Santos, Raúl Rios-Toledo, German Posadas-Durán, Juan Pablo Francisco |
author_sort | Pech-May, Fernando |
collection | PubMed |
description | Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops. |
format | Online Article Text |
id | pubmed-9268769 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92687692022-07-09 Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine Pech-May, Fernando Aquino-Santos, Raúl Rios-Toledo, German Posadas-Durán, Juan Pablo Francisco Sensors (Basel) Article Crops and ecosystems constantly change, and risks are derived from heavy rains, hurricanes, droughts, human activities, climate change, etc. This has caused additional damages with economic and social impacts. Natural phenomena have caused the loss of crop areas, which endangers food security, destruction of the habitat of species of flora and fauna, and flooding of populations, among others. To help in the solution, it is necessary to develop strategies that maximize agricultural production as well as reduce land wear, environmental impact, and contamination of water resources. The generation of crop and land-use maps is advantageous for identifying suitable crop areas and collecting precise information about the produce. In this work, a strategy is proposed to identify and map sorghum and corn crops as well as land use and land cover. Our approach uses Sentinel-2 satellite images, spectral indices for the phenological detection of vegetation and water bodies, and automatic learning methods: support vector machine, random forest, and classification and regression trees. The study area is a tropical agricultural area with water bodies located in southeastern Mexico. The study was carried out from 2017 to 2019, and considering the climate and growing seasons of the site, two seasons were created for each year. Land use was identified as: water bodies, land in recovery, urban areas, sandy areas, and tropical rainforest. The results in overall accuracy were: 0.99% for the support vector machine, 0.95% for the random forest, and 0.92% for classification and regression trees. The kappa index was: 0.99% for the support vector machine, 0.97% for the random forest, and 0.94% for classification and regression trees. The support vector machine obtained the lowest percentage of false positives and margin of error. It also acquired better results in the classification of soil types and identification of crops. MDPI 2022-06-23 /pmc/articles/PMC9268769/ /pubmed/35808225 http://dx.doi.org/10.3390/s22134729 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Pech-May, Fernando Aquino-Santos, Raúl Rios-Toledo, German Posadas-Durán, Juan Pablo Francisco Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
title | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
title_full | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
title_fullStr | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
title_full_unstemmed | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
title_short | Mapping of Land Cover with Optical Images, Supervised Algorithms, and Google Earth Engine |
title_sort | mapping of land cover with optical images, supervised algorithms, and google earth engine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9268769/ https://www.ncbi.nlm.nih.gov/pubmed/35808225 http://dx.doi.org/10.3390/s22134729 |
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